Ledford H (2011) Melanoma drug wins US approval. Nature 471:561–561
Article PubMed CAS Google Scholar
Marin-Acevedo JA, Kimbrough EO, Lou Y (2021) Next generation of immune checkpoint inhibitors and beyond. J Hematol Oncol 14:45
Article PubMed PubMed Central CAS Google Scholar
Chamoto K, Hatae R, Honjo T (2020) Current issues and perspectives in PD-1 blockade cancer immunotherapy. Int J Clin Oncol 25:790–800
Article PubMed PubMed Central Google Scholar
Long L et al (2018) The promising immune checkpoint LAG-3: from tumor microenvironment to cancer immunotherapy. Genes Cancer 9:176–189
Article PubMed PubMed Central CAS Google Scholar
Mortezaee K, Majidpoor J (2023) Mechanisms of CD8+ T cell exclusion and dysfunction in cancer resistance to anti-PD-(L)1. Biomed Pharmacother 163:114824
Horita S et al (2016) High-resolution crystal structure of the therapeutic antibody pembrolizumab bound to the human PD-1. Sci Rep 6:35297
Article PubMed PubMed Central CAS Google Scholar
Zhang A et al (2024) Regulatory T cells in immune checkpoint blockade antitumor therapy. Mol Cancer 23:251
Article PubMed PubMed Central Google Scholar
Wei Y, Li Z (2022) LAG3-PD-1 combo overcome the disadvantage of drug resistance. Front Oncol. https://doi.org/10.3389/fonc.2022.831407
Article PubMed PubMed Central Google Scholar
Raybould MIJ et al (2020) Thera-SAbDab: the therapeutic structural antibody database. Nucleic Acids Res 48(D1):D383–D388
Article PubMed CAS Google Scholar
Khoja L, Butler MO, Kang SP, Ebbinghaus S, Joshua AM (2015) Pembrolizumab. J Immunother Cancer 3:36
Article PubMed PubMed Central Google Scholar
Naing A et al (2020) A first-in-human phase 1 dose escalation study of spartalizumab (PDR001), an anti–PD-1 antibody, in patients with advanced solid tumors. J Immunother Cancer 8:e000530
Article PubMed PubMed Central Google Scholar
Al-Khami AA et al (2020) Pharmacologic properties and preclinical activity of sasanlimab, a high-affinity engineered anti-human PD-1 antibody. Mol Cancer Ther 19:2105–2116
Article PubMed CAS Google Scholar
Hamid O et al (2022) 400P phase I study of fianlimab, a human lymphocyte activation gene-3 (LAG-3) monoclonal antibody, in combination with cemiplimab in advanced melanoma (mel). Ann Oncol 33:S1598
Schöffski P et al (2022) Phase I/II study of the LAG-3 inhibitor ieramilimab (LAG525) ± anti-PD-1 spartalizumab (PDR001) in patients with advanced malignancies. J Immunother Cancer 10:e003776
Article PubMed PubMed Central Google Scholar
Paik J (2022) Nivolumab plus relatlimab: first approval. Drugs 82:925–931
Article PubMed CAS Google Scholar
Abanades B et al (2023) Immunebuilder: Deep-learning models for predicting the structures of immune proteins. Commun Biol 6:575
Article PubMed PubMed Central CAS Google Scholar
Tan S et al (2017) An unexpected N-terminal loop in PD-1 dominates binding by nivolumab. Nat Commun 8:14369
Article PubMed PubMed Central CAS Google Scholar
Yang Z et al (2012) UCSF chimera, MODELLER, and IMP: an integrated modeling system. J Struct Biol 179:269–278
Article PubMed CAS Google Scholar
Krieger E et al (2009) Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: four approaches that performed well in CASP8. Proteins Struct Funct Bioinform 77:114–122
Dauparas J et al (2022) Robust deep learning–based protein sequence design using ProteinMPNN. Science 378:49–56
Article PubMed PubMed Central CAS Google Scholar
Grudman S, Fajardo JE, Fiser A (2021) Intercaat: identifying interface residues between macromolecules. Bioinformatics 38:554–555
Article PubMed Central Google Scholar
Schymkowitz J et al (2005) The FoldX web server: an online force field. Nucleic Acids Res 33:W382-388
Article PubMed PubMed Central CAS Google Scholar
Buß O, Rudat J, Ochsenreither K (2018) FoldX as protein engineering tool: better than random based approaches? Comput Struct Biotechnol J 16:25–33
Article PubMed PubMed Central Google Scholar
Sapozhnikov Y, Patel JS, Ytreberg FM, Miller CR (2023) Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability. BMC Bioinformatics 24:426
Article PubMed PubMed Central CAS Google Scholar
Pearce R, Huang X, Setiawan D, Zhang Y (2019) Evodesign: designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function. J Mol Biol 431:2467–2476
Article PubMed PubMed Central CAS Google Scholar
Vangone A, Bonvin AMJJ (2017) PRODIGY: a contact-based predictor of binding affinity in protein-protein complexes. Bio-Protoc 7:e2124
PubMed PubMed Central Google Scholar
Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A (2016) PRODIGY: a web server for predicting the binding affinity of protein–protein complexes. Bioinformatics 32:3676–3678
Article PubMed CAS Google Scholar
Yang YX, Huang JY, Wang P, Zhu BT (2023) Area-affinity: a web server for machine learning-based prediction of protein-protein and antibody-protein antigen binding affinities. J Chem Inf Model 63:3230–3237
Article PubMed PubMed Central CAS Google Scholar
Pires DEV, Ascher DB (2016) Mcsm-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res 44:W469-473
Article PubMed PubMed Central CAS Google Scholar
Case DA et al (2023) AmberTools. J Chem Inf Model 63:6183–6191
Article PubMed PubMed Central CAS Google Scholar
Tian C et al (2020) ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J Chem Theory Comput 16:528–552
Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105:9954–9960
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